Deep Learning
Deep Learning (Labs)
Lab Syllabus
| Sr. No. | Title of the experiment | CO |
|---|---|---|
| 1 | Explore Python libraries (NumPy, Pandas, TensorFlow, Keras, PyTorch) for deep learning. | CO1 |
| 2 | Implement Perceptron algorithm to simulate basic logic gates. | CO2 |
| 3 | Implement a backpropagation algorithm to train a DNN with at least 2 hidden layers. | CO2 |
| 4 | Design and implement a CNN model for image classification. | CO3 |
| 5 | Design and implement an RNN for classification of temporal data. | CO4 |
| 6 | Write a program for Time-Series Forecasting with the LSTM Model. | CO4 |
| 7 | Design the architecture and implement the auto encoder model for image compression. | CO5 |
| 8 | Write a program to detect Dog image using YOLO Algorithm. | CO6 |
| 9 | Write a program for character recognition using RNN and compare with CNN. | CO4 |
| 10 | Write a program to develop a GAN for Generating MNIST Handwritten Digits. | CO6 |
| 11 | Lab Project |
Deep Learning Lab Manual
Experiments Backup Repository
Practicals Notice 2026 (AIML-B)
DL OR & PR exam
Batch: B1 Time: 9 am - 1 pm Venue: 301
Instructions:
- Hall ticket and college ID card are compulsory. If there is any issue with the hall ticket, a written permission from HOD-CSE is mandatory.
- File must be certified.
- Mobile phone, smartwatch, or other electronic devices (except calculator) are not allowed inside the exam hall.
- Critical defaulters must carry a duly signed undertaking application, as per the department policy.
Exam pattern:
- Each student will have to perform an experiment (allotted randomly) from the lab file.
- Oral will be based on the entire syllabus. Preferably prepare well at least 3-4 topics, each from a different chapter.
Experiment 1
Explore Python libraries for Deep Learning
Experiment 2
AND GATE, OR GATE, NOT GATE, Setosa vs Versicolor
Experiment 3
Implementing a Deep Feed Forward ANN with Backpropagation
Experiment 4
Design and implement a CNN model for image classification.
Experiment 5
Design and implement an RNN for classification of temporal data.
Experiment 6
Time-Series Forecasting with the LSTM Model
Experiment 7
Design and implement an autoencoder model for image compression
Experiment 8
Detect Dog image using the YOLO algorithm
Experiment 9
Character recognition using RNN and comparison with CNN
Experiment 10
Build a GAN for generating MNIST handwritten digits.
Lab Project
Final report submission for the Deep Learning lab project.